Emulsion polymerizations particle size optimization

Research on the modelling, optimization and control of emulsion polymerization (latex) reactors and processes has been expanding rapidly as the chemistry and physics of these systems become better understood, and as the demand for new and improved latex products increases. The objectives are usually to optimize production rates and/or to control product quality variables such as polymer particle size distribution (PSD), particle morphology, copolymer composition, molecular weights (MW s), long chain branching (LCB), crosslinking frequency and gel content. 

The latexes upon which this industry developed were natural rubber and polychloroprene for solvent resistance. However, technology is advancing to permit penetration of carboxylated nitrile latex for optimized solvent resistance and tougher abrasion resistance. Among the competition to latexes in this field are poly(vinyl chloride) plastisols. As technology develops in producing small particle size latexes from polymers whose synthesis is loo water-sensitive for emulsion polymerization, the dipped goods industry will quickly convert to their utilization from the solvent-based cements of these polymers now employed Prime candidates include butyl rubber, EPDM, hypalon, and vlton. 

HIPS) is produced commercially by the emulsion polymerization of styrene monomer containing dispersed particles of polybutadiene or styrene-butadiene (SBR) latex. The resulting product consists of a glassy polystyrene matrix in which small domains of polybutadiene are dispersed. The impact strength of HIPS depends on the size, concentration, and distribution of the polybutadiene particles. It is influenced by the stereochemistry of polybutadiene, with low vinyl contents and 36% d5-l,4-polybutadiene providing optimal properties. Copolymers of styrene and maleic anhydride exhibit improved heat distortion temperature, while its copolymer with acrylonitrile, SAN — typically 76% styrene, 24% acrylonitrile — shows enhanced strength and chemical resistance. The improvement in the properties of polystyrene in the form of acrylonitrile-butadiene-styrene terpolymer (ABS) is discussed in Section VILA. 

The dependence of the risk parameters on process variables such as the concentrations of monomer, polymer, initiator or catalyst, solvent, water and particle size (in emulsion) and MWD are of paramount importance to establish the safe operation regions of polymerization reactors, and furthermore to develop optimal control strategies imder safe conditions. The maximum pressure, Pmax> and maximum temperature, Tmax achieved during the runaway depends on the process conditions (e.g., the higher the amount of monomer in the reactor and the process temperature, the higher Pmax and Tmax)- Also important is the rate at which the runaway reaches the maximum pressures and temperatures. This rate will provide an indication of the time that the operator/control system of the plant has to react in order to keep the polymerization imder safe conditions. 

In copolymerization, the more reactive monomer may be added to the reactor over time to produce a more uniform copolymer composition distribution. This may be done by feeding comonomer at fixed rates, by adding various comonomers at predetermined times, or by following a complex monomer addition policy determined by off-line optimization of a mathematical model of the polymerization process. If copolymer composition is measured or estimated on-line, the reactive monomer can be added in a closed-loop fashion [35]. In emulsion polymerization, surfactant may be added over time to control the formation of new particles, and hence the particle size distribution (PSD) [36]. 

Pollock, M. J., MacGregor, J. F., and Hamielec, A. E. (1981) Continuous poly (vinyl acetate) emulsion polymerization reactors dynamic modeling of molecular weight and particle size development and application to optimal multiple reactor system design. Computer Applications in Applied Polymer Science, (ed. T. Provder), ACS, Washington, pp. 209-20. 

The control of the particle size in emulsion polymerization using closed-loop strategies is a very attractive yet challenging problem [1]. Difficulties associated with online measurement of the particle size together with the complex mechanisms involved in emulsion polymerization systems limit the options and make control implementation a formidable task. In many cases, conventional optimization strategies fail to ensure a consistent product quality with the result that industries rely on traditional recipes and experience. 

In general, the optimization of polymerization processes [2] focuses on the determination of trade-offs between polydispersity, particle size, polymer composition, number average molar mass, and reaction time with reactor temperature and reactant flow rates as manipulated variables. Certain approaches [3] apply nonhnear model predictive control and online, nonlinear, inferential feedback control [4] to both continuous and semibatch emulsion polymerization. The objectives include the control of copolymer composition. 

The foregoing indicates that the implementation of process optimization for the optimal control of emulsion polymerization processes is relying on technological advancement in the areas of mathematical process modeling, soft-sensing, and model-based control. This chapter illustrates some of the most successful approaches and their application to the control of the particle size in emulsion polymerization. 

Due to the complex nature of the emulsion polymerization process and the associated set of distributed and lumped equations, the control vector parameterization approach can be adopted along with the -constraint optimization technique in a multiobjective form to account for the various control objectives. Several constraints should be included to define the desired final particle size and to account for different process and recipe limitations. 

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